Application of genetic algorithm for modeling of dense packing of concrete aggregates

Соболев, Константин; Amirjanov, Adil

doi:10.1016/j.conbuildmat.2010.01.010

Cited by 66 publications

(39 citation statements)

References 23 publications

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“…Very early reports have already emphasized the important effects of the aggregates grading on the concrete performance [1,2]. The problem of the best proportion for aggregate has been the subject of many experimental and theoretical investigations [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The first attempt to provide the best optimal particle size distribution were based on trials with different diameter spherical balls [2,7,8].…”

Section: Introductionmentioning

confidence: 99%

Packing degree optimization of arbitrary circle arrangements by genetic algorithm

Rahmani

2014

Granular Matter

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Selection of optimal aggregate proportions is the main part of the concrete mix design optimization. Assuming circular aggregates, a new Sequential Packing Algorithm (SPA) is proposed to densify arrangement of arbitrary circles. To achieve the densest packing of circles, the porosity packed circle assemblies is optimized by a Genetic Algorithm (GA) search module. Proper adjustment of the algorithm's parameters and selection of the initial population are effective tools for speeding up the computational progress. The model exactly solves known problems. Finally, Ideal-grading curve is presented after the implementation of GA on the data set.

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Section: Introductionmentioning

confidence: 99%

Packing degree optimization of arbitrary circle arrangements by genetic algorithm

Rahmani

2014

Granular Matter

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“…However, in concrete, these savings have an impact of the compressive strength. Results would be improved using a better graded aggregate corresponding with a Fuller-Thompson grading curve [24]. This would also improve the performance for RCC constructed below an asphalt surface course.…”

Section: Compressive Strengthmentioning

confidence: 99%

Structural Properties of Concrete Materials Containing RoadCem

Holmes¹

2015

Journal of Construction Engineering

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This paper presents findings from a preliminary study to assess the structural and material properties of a nonstandard, concrete type mix containing RoadCem, a traditional soil stabilising additive. Two different mixes determined the effect of adding RoadCem in terms of compressive and flexural strengths, breaking strain, thermal expansion and contraction behaviour, permeability using a falling head, and Young's modulus. RoadCem is a fine powder containing alkali metals and synthetic zeolites which are complemented with a complex activator. RoadCem modifies the dynamics and chemistry of cement hydration by enhancing the crystallisation process and forming longer needle crystalline structures. It reduces the heat of hydration with an early strength development. Varying the volume in the mix varies the viscosity and alters curing times while maintaining the water cement ratio. The results from this study have shown a modest increase in compressive strength and Young's modulus with improvements in thermal performance, particularly at low temperatures. The flexural strength of the two mixes was similar with a much reduced permeability in the RoadCem mix. The results demonstrate the improved performance of concrete incorporating RoadCem but further improvements are possible by using a better graded aggregate and controlling the maximum dry density and moisture contents.

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“…In this study, a packing theory used by Sobolev and Amirjanov [26] is utilized to generate the virtual specimens. In their model, particle packings with three different size distributions, such as mono size, bimodal, and Gaussian, can be produced; here, the modified bimodal distribution model is adopted, and a detailed description of the model can be found in [26,36].…”

Section: Virtual Specimens With Different Gradingsmentioning

confidence: 99%

“…To investigate the effect of the gradings on the material properties, elastic modulus, compressive strength, and thermal conductivity values of lightweight concrete specimens are evaluated using experimental and numerical approaches; experimental tools, such as testing compressive strength (Toni Technik, Berlin, Germany) and thermal conductivity by the transient plane source method (Hot Disk, Göteborg, Sweden), are adopted to measure the mechanical and thermal properties, respectively. For the numerical lightweight specimens, random packing models of particles with different size distributions are adopted here [24][25][26], and their responses are computed using finite element (FE) analysis. In addition, X-ray computed tomography (CT) is utilized to investigate the inner structures of the lightweight concrete specimen without destruction of the samples; the aggregate structures are investigated by incorporating a probabilistic description method, the lineal-path function, to describe the size characteristics of the lightweight aggregates within the specimens.…”

Section: Introductionmentioning

confidence: 99%

Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete

2017

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Lightweight aggregate concrete is a material with very low density and good thermal insulation, and several types of lightweight aggregates have been used for lightweight concrete. Since the characteristics of lightweight aggregates strongly affect the properties of lightweight concrete, a proper consideration for the use of lightweight aggregate is very important for development of lightweight materials. In particular, the sizes and spatial distributions of lightweight aggregates can influence the material responses of lightweight concrete, such as compressive strength and thermal conductivity. In this study, different types of gradings of lightweight aggregates are adopted to investigate the effect of gradings on the material properties. Liaver R , an expanded glass granulate, is used as a lightweight aggregate for the specimens. Virtual models of the lightweight specimens with different gradings are numerically generated, and both mechanical and thermal properties are evaluated using experimental and numerical approaches for more detailed investigation. The obtained results can be utilized to suggest an optimal grading that satisfies both the mechanical and thermal properties of lightweight concrete specimen.

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Application of genetic algorithm for modeling of dense packing of concrete aggregates

Cited by 66 publications

References 23 publications

Packing degree optimization of arbitrary circle arrangements by genetic algorithm

Packing degree optimization of arbitrary circle arrangements by genetic algorithm

Structural Properties of Concrete Materials Containing RoadCem

Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete

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